Data Engineering in FinTech: Planning a Successful Data Audit
Bottom Line Up Front: A well-planned data audit is your roadmap to successful big data projects in FinTech. It identifies data quality issues, ensures regulatory compliance, and establishes the foundation for data-driven decision making that can increase customer retention and reduce operational costs. As a FinTech executive, you’ve likely heard the statistics: companies that leverage customer data effectively can increase customer retention and value significantly. But before you can harness the power of big data analytics, predictive modeling, or AI-driven insights, you need to ensure your data foundation is solid. This is where a comprehensive data audit becomes critical. A data audit isn’t just a technical exercise—it’s a strategic business initiative that can make or break your big data project’s success. Here’s your executive guide to planning and executing a data audit that sets your organization up for data engineering success. Why Data Audits Are Non-Negotiable in FinTech Unlike other industries, FinTech operates in a heavily regulated environment where data privacy, AML compliance, and customer protection requirements are stringent and constantly evolving. A data audit serves three critical business purposes: Risk Mitigation: Poor data quality can lead to regulatory violations, customer churn, and reputational damage. Companies with robust internal controls see a 50% reduction in fraud losses and duration. Strategic Foundation: Clean, well-organized data enables advanced analytics that can detect customer segments most likely to churn and identify retention opportunities. Cost Efficiency: Addressing data issues early prevents expensive rework and ensures your big data investments deliver measurable ROI. The Executive’s 6-Step Data Audit Framework 1. Define Business Objectives and Scope Start by clearly articulating what you want to achieve with your big data project. Are you looking to improve customer segmentation, enhance fraud detection, or optimize operational efficiency? Your audit scope should align with these objectives. Action Items: 2. Assemble Your Cross-Functional Audit Team A successful data audit requires both business and technical expertise. Your team should include representatives from: 3. Conduct a Comprehensive Data Inventory This step involves cataloging all data sources, understanding data flows, and identifying potential integration challenges. Consolidating multiple data sources to create unified customer profiles is often a primary goal, but first you need to know what you’re working with. Key Questions to Address: 4. Assess Data Quality and Compliance This is where technical expertise becomes crucial. Financial services companies must ensure data accuracy, completeness, and compliance with regulations like AML, KYC, and data privacy laws. Critical Evaluation Areas: 5. Identify Integration and Architecture Requirements Building scalable data architectures tailored to each enterprise enables advanced analytics on vast and complex datasets. Your audit should reveal what infrastructure changes are needed to support your big data objectives. Strategic Considerations: 6. Create an Action Plan with Clear Priorities Transform your audit findings into a prioritized roadmap. Focus on high-impact, low-complexity improvements first to build momentum and demonstrate value. Common Data Audit Risks to Avoid Underestimating Timeline and Resources: Data audits often reveal more complexity than initially anticipated. Build buffer time into your project schedule. Neglecting Change Management: Employee training on regulatory requirements, internal policies, and best practices is essential. Your audit may identify process changes that require significant organizational adjustment. Focusing Only on Technical Issues: Remember that data problems often stem from business process issues. Address both technical and procedural root causes. Leveraging External Expertise Many FinTech organizations benefit from partnering with specialized data engineering firms during the audit process. Expert teams can help enterprises connect fragmented data sources into unified ecosystems and develop efficient ETL pipelines that turn raw data into strategic assets. When evaluating external partners, look for firms with: Measuring Audit Success Your data audit should deliver concrete, measurable outcomes: Immediate Deliverables: Long-term Success Metrics: Moving Forward with Confidence A well-executed data audit transforms uncertainty into clarity, giving you the confidence to invest in big data initiatives that drive real business value. When data is properly activated, it transforms the nature of work and brings competitive advantages that can differentiate your organization in the crowded FinTech marketplace. Remember, the goal isn’t perfection—it’s progress. Your data audit should provide a clear path forward, enabling you to make informed decisions about technology investments, resource allocation, and strategic priorities. By following this framework and partnering with experienced data engineering professionals, you’ll be well-positioned to launch big data projects that deliver measurable business impact while maintaining the security and compliance standards that FinTech customers and regulators demand. Ready to start your data audit journey? Deegloo’s team of data engineering experts specializes in helping FinTech companies consolidate data sources, build scalable architectures, and transform raw data into strategic business assets. Contact us to discuss how we can support your big data project success.
ETL vs ELT: Choosing the Right Data Pipeline Strategy for Your Business
The way you move and process information can make or break your competitive advantage. Whether in a fintech startup processing thousands of transactions per minute, a logistics company tracking shipments across continents, or any company managing customer data at scale, your data pipeline architecture is a critical business decision. Two dominant approaches have emerged in the data engineering world: ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). While these acronyms might sound like technical jargon, understanding the difference between them is crucial for any decision maker responsible for data strategy, product development, or digital transformation initiatives. Understanding ETL: The Traditional Approach ETL has been the gold standard for data processing for decades. Think of it as a quality control assembly line where data goes through a rigorous transformation process before reaching its final destination. Extract: Data is pulled from various source systems like your CRM, payment processors, mobile apps, or IoT sensors. Transform: The data is cleaned, standardized, and restructured in a separate processing environment. This is where business rules are applied, data quality checks are performed, and information is formatted to meet specific requirements. Load: Only after transformation is complete does the data enter your data warehouse or analytics platform. For a fintech company, this might mean extracting transaction data from multiple payment gateways, applying fraud detection algorithms and compliance rules during transformation, then loading the clean, standardized data into a warehouse for regulatory reporting and analytics. The ELT Revolution: Speed Meets Flexibility ELT flips the traditional script by leveraging the power of modern cloud data warehouses. Instead of transforming data before it arrives, raw data is loaded first and transformed within the destination system. Extract: Data is pulled from source systems, just like in ETL. Load: Raw data is immediately loaded into a powerful data warehouse or data lake. Transform: Transformations happen within the destination system using its computational power. Consider a logistics company tracking millions of shipment events. With ELT, all GPS coordinates, delivery confirmations, and sensor data can be immediately loaded into a cloud warehouse, then transformed on-demand to create real-time dashboards for different stakeholders. Why Neither Approach is Universally Better The key insight that many organizations miss is that ETL and ELT aren’t competing solutions—they’re different tools designed for different business contexts and requirements. ETL excels when: ELT thrives when: For telecommunications companies, the choice often depends on the specific use case. Customer billing data might benefit from ETL’s rigorous transformation process to ensure accuracy and compliance, while network performance data might be better suited to ELT for real-time monitoring and rapid analysis. Industry-Specific Considerations While both ETL and ELT have their place across all sectors, certain industries show natural preferences based on their core business requirements, regulatory environment, and operational characteristics. However, it’s important to recognize that even industries with strong leanings toward one approach typically implement both methodologies to address different aspects of their data ecosystem. Fintech: The financial services industry traditionally favors ETL due to stringent regulatory requirements and the critical importance of data accuracy. Banking regulations, anti-money laundering (AML) compliance, and financial reporting demand well-structured, validated data pipelines. ETL’s approach of cleaning and transforming data before loading ensures that regulatory reports are built on verified, compliant data. However, the modern fintech landscape is pushing organizations toward hybrid approaches. Real-time fraud detection systems benefit enormously from ELT’s ability to process high-velocity transaction streams immediately. A typical fintech company might use ETL for their core banking operations, regulatory reporting, and customer onboarding processes, while implementing ELT for real-time risk scoring, personalized product recommendations, and operational dashboards. For example, a digital payment platform might employ ETL to ensure all transaction records meet regulatory standards for their nightly compliance reports, while simultaneously using ELT to analyze spending patterns in real-time for fraud detection and personalized offers. Transport and Logistics: This industry has a natural affinity for ELT due to the massive volumes of IoT data generated by vehicles, sensors, warehouses, and tracking systems. The ability to immediately ingest GPS coordinates, delivery confirmations, temperature readings, and traffic data enables real-time operational decision-making that’s critical for competitive advantage. ELT excels in logistics for supply chain optimization, route planning, and predictive maintenance, where speed to insight directly translates to operational efficiency and cost savings. However, ETL remains essential for structured business processes like invoicing, customer billing, and regulatory compliance (especially for hazardous materials transport or international shipping documentation). A global logistics company might use ELT to process millions of daily shipment tracking events for real-time visibility dashboards, while relying on ETL for structured processes like customs documentation, carrier billing reconciliation, and safety compliance reporting. The combination allows them to maintain operational agility while ensuring business-critical processes remain accurate and compliant. The Hidden Costs of the Wrong Choice Choosing the wrong approach isn’t just a technical misstep—it’s a business risk that can impact your bottom line, regulatory compliance, and competitive position. Organizations that implement ETL when they need ELT often find themselves struggling with: Conversely, companies that choose ELT without considering their specific requirements may face: Making the Strategic Decision The choice between ETL and ELT—or more often, the right combination of both—requires deep understanding of your business context, technical constraints, and future growth plans. It’s not a decision that should be made in isolation by the IT department or driven solely by vendor preferences. Key factors to evaluate include: Your Next Steps Whether you’re embarking on a digital transformation journey, modernizing legacy data systems, or scaling your analytics capabilities, the ETL vs ELT decision will significantly impact your success. The wrong choice can result in wasted resources, missed opportunities, and technical debt that becomes increasingly expensive to resolve. At Deegloo, we understand that data architecture decisions are business decisions. Our team of experienced data engineers and business consultants has helped organizations across fintech, transport and logistics, insurance, and telecommunications industries navigate these complex choices. We don’t believe in one-size-fits-all solutions. Instead, we work closely with your team to understand your unique business context, technical constraints, and
Product Announcement – Welcome Retentive
Financed by EU – NextGeneration EU Project name: Retentive.io – Innovative AI solution for reducing churn using causality and prescriptive analyticsProject code: NPOO.C3.2.R3-I1.06.0165 We are happy to announce we have been awarded EU funding to support the development of our innovative new product, Retentive.io. Retentive is an AI-powered solution designed to help businesses in insurance, telecommunications, and financial services reduce customer churn by uncovering the true causes behind customer behavior and generating optimal intervention strategies. The project will build on open-source technologies and cutting-edge causal AI research, enabling businesses not only to predict which customers are at risk of leaving, but also to understand why and how to most effectively retain them. Through advanced features such as causal modeling, explainable AI, and what-if scenario analysis, Retentive will provide organizations with actionable insights and measurable impact on customer loyalty and long-term revenue growth. Securing EU funding represents an important milestone for Deegloo, reinforcing its commitment to driving innovation and contributing to the wider adoption of open-source technologies in the field of causal AI. This support will accelerate the development of Retentive and strengthen Deegloo’s capacity to bring advanced, explainable AI solutions to global markets. Further updates on the development of Retentive and its upcoming launch will be announced in the coming months. Total value of the project: 71.329,64€ Co-financed by EU: 49.930,75€ Project timeframe: September 1st, 2025. – June 1st, 2026. Contact for More Information: Matija Martekmatija@deegloo.com
5 Critical Pitfalls That Sink Data Projects (And How to Avoid Them)
Too many promising projects crash and burn despite good intentions and substantial budgets. The statistics are sobering: over 85% of data science projects fail to deliver their intended business value, with only 4% of companies successfully deploying ML models to production. At Deegloo, we’ve built our expertise around efficient ETL and ELT pipelines that turn raw data into strategic assets, powering and scaling insights, automation, and smarter decisions. But technical prowess alone isn’t enough. Success requires the right blend of pragmatic project management and robust system architecture—avoiding the pitfalls that consistently derail data initiatives. Drawing from real-world lessons and industry best practices, here are the five critical pitfalls that sink data projects and how to steer clear of them. Pitfall 1: Rushed Code and Poor Planning Leading to Technical Debt The pressure to deliver quickly often creates a false economy. Teams prioritize speed over sustainability, accumulating what Martin Fowler calls “technical debt”—the implied cost of choosing expedient solutions over robust ones. Technical debt in software development means favoring speed and delivery over the quality and longevity of a software solution. As detailed in analysis of the technical debt quadrant, the most dangerous type is what Fowler categorizes as “deliberate and reckless”—when teams knowingly prioritize speed over quality without proper risk awareness or payback plans. Common causes include: The Project Manager’s Solution: Plan sprints that explicitly balance delivery speed with code quality. Build “debt payback” sessions into your roadmap from day one. As a PM, your job isn’t just to deliver fast—it’s to deliver sustainably. Create a technical debt register and make it visible to stakeholders. When business pressure mounts, use concrete examples of how previous rushed decisions cost the team velocity later. Technical debt will most certainly get in the way of growth, not only in terms of delivering software that will possibly have issues later on but also in terms of limiting the growth of developers themselves. Pitfall 2: Over-engineering and Misplaced Technical Priorities The allure of shiny new technologies can be irresistible, especially in the data engineering space. But over-engineering solutions for problems that need simpler approaches wastes resources and adds unnecessary complexity. It’s easy to get swept up by new and exciting technology. It’s also common for businesses to over-engineer solutions to problems that would get more benefit from a simpler solution. In data projects, this often manifests as experimenting with cutting-edge technologies that don’t solve the actual problem or add unnecessary complexity to already challenging implementations. Small implementation mistakes can have massive performance implications. Even seemingly minor code patterns, when applied at scale across large datasets, can drastically impact system performance and reliability. The Architectural Approach: Always start with the simplest solution that meets your requirements. Ask yourself: “Will this technology help solve the actual problem, or are we just being seduced by novelty?” Follow these principles: Pitfall 3: Neglecting Data Lineage and Transparency Data lineage—tracking where data comes from, how it transforms, and where it goes—is often treated as a nice-to-have rather than a fundamental requirement. This oversight creates cascading problems for governance, troubleshooting, and trust. Data lineage tracks the entire data lifecycle, which helps in finding the root cause of issues. A good catalog automates data quality tasks to monitor, identify, and prevent data quality issues on both column and data asset level. It also tracks data lineage through the entire data lifecycle, which helps in finding the root cause of the issue. Without proper lineage tracking, teams spend countless hours troubleshooting data quality issues, struggling to trace problems back to their source. Worse, business users lose confidence in data when they can’t understand its provenance. According to insights on data veracity, to trust data, you have to be certain of the data sources it came from—it takes a lot of time to track down where some data originated, so data governance and lineage are extremely important in ensuring veracity. The Governance Strategy: Embed lineage tracking into your data architecture from day one, not as an afterthought. Modern data platforms make this increasingly straightforward, but it requires intentional design decisions. Key implementation approaches: Remember: if you are not sure of data sources and their correctness, you can not guarantee good results in future projects and utilization. Pitfall 4: Building Without Scalability or Governance in Mind Many data projects start as proof-of-concepts with clean, simplified datasets. When these pilots need to scale to production with real-world data complexity, they often fail catastrophically. The challenge is multi-faceted: Data is spread across multiple databases in multiple formats not suitable for analytics. Many companies lack data infrastructure or do not have enough volume or quality data. Additionally, many organizations start projects with very sanitized, simplified data. Then, after expectations have already been set with the model tested in a sandbox, they experience great disappointment when the solution fails to generalize to the real world. Poor data governance compounds these issues. Without established quality standards, validation rules, and monitoring systems, data drift and quality degradation go unnoticed until they cause production failures. The Scalability Framework: Define your scalability targets upfront—not just data volumes, but also complexity, user concurrency, and integration requirements. Design your architecture with partitioning, parallel processing, and distributed storage in mind from the beginning. Key strategies: Pitfall 5: Undefined Business Outcomes and Misaligned Objectives Perhaps the most fundamental pitfall: lack of clear business objectives. Data scientists need to understand the business goals of a project and the value it will add to the organization. Without clear objectives, it is challenging to define the right problem and determine what data is required. Too many data projects begin with vague mandates like “use our data better” or “implement AI.” Without concrete business outcomes, teams optimize for technical metrics that may be irrelevant to actual business value. Much work needs to be done to collect, normalize, and aggregate data. First-timers often make the mistake of not realizing that the bulk of the heavy-lifting in your “cool” data science project is getting the data lined up. The Business Alignment Solution:
Executive Guide to High-Performance Analytics Dashboards: Driving Competitive Advantage Through Data Speed and Reliability
The Business Case for Lightning-Fast Dashboards Consider this scenario: Your sales director needs to make a critical pricing decision during a competitor announcement. They open your analytics dashboard to review current market position, customer segments, and pricing elasticity data. If that dashboard takes 30 seconds to load or shows outdated information, your competitor gains the advantage while your team waits for insights. This challenge becomes even more critical in complex operational environments. Take the case of a supply chain management company that was drowning in disconnected analytical tools used by different departments. Each system generated conflicting insights, making decision-making more difficult rather than easier. The solution required consolidating multiple data sources into unified dashboards that could instantly highlight outliers, discrepancies, and opportunities across their entire supply chain network. Research demonstrates that business users abandon digital tools that take longer than 3 seconds to load, and executives frequently make suboptimal decisions when dashboards fail to deliver timely, reliable information. In today’s competitive landscape, dashboard performance isn’t just a technical concern—it’s a strategic imperative that directly impacts decision-making speed and business agility. This guide provides business leaders with the essential knowledge to ensure their analytics investments deliver maximum return through superior performance and reliability. The Strategic Foundation: Why Dashboard Performance Matters Business Impact of Poor Dashboard Performance Lost Productivity: When dashboards load slowly, employees lose momentum and focus. A 10-second delay multiplied across hundreds of daily dashboard interactions represents significant productivity loss. Delayed Decision Making: In fast-moving markets, outdated or slow-loading data creates decision paralysis. Competitors operating with faster insights gain sustainable advantages. Reduced User Adoption: Employees naturally avoid tools that frustrate them. Poor-performing dashboards see declining usage, undermining your data-driven culture initiatives. Increased IT Costs: Poorly designed dashboards consume excessive server resources, driving up infrastructure costs while delivering inferior user experience. Essential Performance Strategies for Business Leaders 1. Smart Data Architecture Decisions Optimize Data Storage Strategy: Rather than storing every transaction detail in your main reporting system, implement intelligent data summarization. For instance, instead of keeping millions of individual sale records for monthly reporting, maintain pre-calculated monthly totals that load instantly. A practical example of this approach can be seen in agricultural operations, where dairy farm management systems consolidate vast amounts of daily data—milk production, feed consumption, herd health, weather conditions, and market prices—into unified dashboards. Rather than forcing farm managers to navigate through multiple spreadsheets with different formats, the system pre-calculates key performance indicators and presents trends and discrepancies through intuitive visualizations, enabling faster decision-making about farm operations. Implement Intelligent Data Refresh: Not all data needs real-time updates. Sales figures might require hourly refreshes, while historical trend data can update daily. Matching refresh frequency to business needs reduces system load and improves performance. Design for Growth: Plan your data architecture to handle 3-5 years of projected growth. It’s more cost-effective to build scalability from the start than to retrofit systems later. 2. Strategic Caching Implementation Multi-Level Performance Optimization: Implement intelligent caching at multiple system levels to dramatically improve response times: Smart Cache Management: Implement automatic cache refresh strategies that balance data freshness with performance. Critical real-time metrics can bypass caching, while stable historical data leverages longer cache periods. 3. Visualization Performance Strategy Choose Appropriate Chart Types: Different visualization types have vastly different performance implications: Implement Intelligent Data Sampling: For large datasets, show summarized views by default with drill-down capabilities for detailed analysis. This approach maintains user experience while managing system resources effectively. 4. Mobile-First Performance Design Responsive Dashboard Architecture: With increasing mobile usage, design dashboards that perform excellently on smartphones first, then enhance for larger screens. This ensures optimal performance across all devices your team uses. Bandwidth Optimization: Implement data compression and intelligent loading strategies that automatically adjust based on connection quality, ensuring consistent experience regardless of location or device. 5. Scalable Technology Architecture Modular System Design: Structure your dashboard systems as independent, scalable components: Automatic Scaling: Implement systems that automatically adjust resources based on usage patterns, ensuring consistent performance during peak times while controlling costs during low-usage periods. Common Performance Pitfalls to Avoid 1. The “Show Everything” Trap Many organizations attempt to display comprehensive data in single dashboard views, creating 30+ second load times and overwhelming users. Instead, implement progressive disclosure—show high-level insights immediately with options to drill down into details. The supply chain visualization solution mentioned earlier demonstrates this principle effectively. Rather than overwhelming dispatchers with every detail of their logistics network, the dashboard initially displays key metrics like dispatch discipline and plant performance. Users can then drill down into specific haulers, loading intervals, or detention tracking when needed. This approach enabled the company to reduce manual work, cut costs, and maintain visibility across their entire supply chain without sacrificing performance. 2. Real-Time Everything Misconception Not every metric requires real-time updates. Distinguish between truly time-sensitive data (like system alerts) and information that can refresh on schedules (like monthly sales trends). This distinction dramatically improves system performance and reduces infrastructure costs. 3. Ignoring Mobile Performance Dashboards designed only for desktop use fail in today’s mobile-first business environment. Ensure your analytics perform well on smartphones and tablets, as many critical business decisions happen outside traditional office settings. 4. Overcomplicating Data Relationships Avoid creating dashboards that require complex calculations every time they load. Pre-calculate common metrics during low-usage periods and store results for instant retrieval during business hours. Evaluation Criteria for Technology Selection As you decided to invest into improving analytical dashboards of your product or your organization, you will face the challenge of choosing the right technologies for optimal impact. There are plenty of open-source and commercial technologies available, and there may not be a clear answer on which one works best. To choose, start with these questions and see where that gets you. Scalability: Can the solution grow with your organization’s data and user base? Integration: How well does it work with your existing technology stack? Total Cost of Ownership: Consider licensing, implementation, training, and ongoing maintenance costs. User Adoption Potential: Will your team actually use this tool
Groundworks for AI Transformation: Make Your Data Make Sense
Introducing artificial intelligence to business is transformative. It brings smarter operations, deeper insights, and competitive advantages that seemed impossible a few years ago. Yet for many organizations, the journey from AI ambition to AI reality remains frustrating. The culprit isn’t the technology itself, but rather the foundation upon which it’s built: data. The numbers tell it all. Research from RAND Corporation shows that more than 80% of AI projects fail, while Gartner reports that as much as 30% of AI projects will be abandoned after proof of concept. Meanwhile, 85% of data science projects fail to deliver value, with the vast majority never making it to production. These statistics aren’t just academic—they represent billions in wasted investment and countless missed opportunities for competitive advantage. Before algorithms can work their magic, before machine learning models can deliver breakthrough insights, your data must first make sense. This fundamental truth separates AI success stories from expensive experiments that never quite deliver on their promise. The Hidden Challenge of AI Implementation The statistics paint a sobering picture of AI implementation reality. The share of businesses scrapping most of their AI initiatives increased to 42% in 2024, up from 17% the year before, according to S&P Global Market Intelligence. Even more alarming, more than 80 percent of AI projects fail—twice the rate of failure of information technology projects that do not involve AI. For data science projects specifically, 85% of data science projects fail, with about 87% of data science projects never deployed according to industry research. Organizations often approach AI transformation backwards. They invest heavily in cutting-edge tools, hire data scientists, and launch ambitious projects—only to discover that their data landscape resembles a sprawling, unmapped territory. Siloed databases, inconsistent formats, missing context, and quality issues create a perfect storm that undermines even the most sophisticated AI initiatives. The reality is stark: artificial intelligence systems are only as intelligent as the data they consume. Feed them fragmented, unreliable, or poorly structured information, and they’ll produce results that are equally problematic. This isn’t just a technical challenge—it’s a business-critical foundation that determines whether AI delivers genuine value or becomes another costly technology experiment. Between 70-85% of GenAI deployment efforts are failing to meet their desired ROI, highlighting the urgent need for organizations to address their data foundations before pursuing AI initiatives. Building Your Data Foundation Creating a solid groundwork for AI transformation requires a systematic approach to making your data make sense. This foundation rests on several key pillars that must be addressed before any AI initiative can succeed. Data Discovery and Inventory The first step involves understanding what data you actually have. Many organizations are surprised to discover the breadth and depth of information scattered across their systems. Customer interactions, operational metrics, financial records, sensor data, and countless other information sources often exist in isolation, creating a fragmented picture that limits AI potential. Comprehensive data discovery means cataloging not just what information exists, but where it lives, how it’s formatted, who owns it, and how it relates to other data sources. This inventory becomes the map for your AI transformation journey, revealing both opportunities and obstacles that lie ahead. Quality and Consistency Data quality issues multiply exponentially when AI systems attempt to process inconsistent information. A customer name spelled differently across systems, missing values that aren’t properly flagged, or timestamps in various formats can derail sophisticated algorithms before they begin their work. Establishing quality standards and consistency protocols ensures that your data speaks the same language across your organization. This means implementing validation rules, standardizing formats, and creating processes for handling exceptions and errors. The goal isn’t perfection—it’s predictable, reliable information that AI systems can confidently process. Integration and Accessibility Siloed data creates artificial barriers that prevent AI systems from developing comprehensive insights. When customer behavior data lives separately from product information, or when operational metrics can’t be correlated with financial performance, AI initiatives operate with an incomplete picture. Breaking down these silos requires both technical integration and organizational coordination. Data pipelines must be established to bring relevant information together, while governance frameworks ensure that access is both secure and efficient. The result is a unified view that enables AI systems to identify patterns and relationships that would be impossible to detect in isolated datasets. Architecture Decisions: The Foundation of Your Foundation Building an effective data foundation requires making critical architectural decisions that will impact your AI capabilities for years to come. Two fundamental choices shape how your data flows and where it lives: the data processing approach (ETL vs ELT) and the storage architecture (data lakes vs warehouses vs lakehouses). ETL vs ELT: Choosing Your Data Processing Strategy The traditional Extract, Transform, Load (ETL) approach transforms data before storing it, ensuring consistency and quality at the point of entry. This method works well when you have clearly defined data requirements and stable transformation rules. ETL provides predictable performance and ensures data quality, making it ideal for structured analytics and reporting use cases. However, ETL can become a bottleneck when dealing with the variety and velocity of data required for modern AI applications. The rigid transformation process may eliminate valuable raw data elements that could prove crucial for machine learning models, and the preprocessing requirements can slow down data ingestion significantly. Extract, Load, Transform (ELT) flips this process, storing raw data first and transforming it as needed. This approach offers greater flexibility for AI applications because it preserves the original data while allowing for multiple transformation strategies. ELT is particularly valuable for exploratory analytics and machine learning, where data scientists need access to raw information to discover patterns that weren’t anticipated during initial data design. The choice between ETL and ELT isn’t binary—many organizations implement hybrid approaches that use ETL for well-understood, high-frequency data processes while employing ELT for exploratory and AI-focused initiatives. The key is matching your processing strategy to your AI objectives and data characteristics. Data Lakes, Warehouses, and Lakehouses: The Storage Dilemma Where you store your data is equally crucial to
How to make customer health scores reliable
Customer health scores are an essential metric for understanding your customer relationships. When used effectively, they bring valuable insights into customer satisfaction, risk of churn, and potential growth opportunities. However, reliability is a common issue with customer health scores. Customer-facing representatives often admit that they do not devote much attention to customer health scores due to the perceived lack of value they bring to their work. Here are five checkpoints to ensure you implemented health scores the right way. Ensure data is up to date The first thing to think about when talking about reliability of healthscores is data accuracy. Using outdated or incorrect information results in misleading health scores, which further leads to misguided actions. By implementing systems that automatically update key data points such as product usage, support interactions, payment history and other relevant information, healthscores become up-to-date and relevant. Additionally, you should schedule a regular audit of your data sources to ensure that they are feeding accurate and timely information into the health score. While regular updates may seem as the obvious first step of a healthy health score, companies often manage to get it wrong due to inadequate IT support, using legacy systems that can’t connect to the health score calculator or underestimated need for computing resources that brings health score calculations to a halt. To ensure the best implementation, it’s recommended to check accessibility of all the necessary systems prior to implementing health scores. Implement Prescriptive Analytics A customer health score should not only reflect the current state but also guide future action. A properly set up health score consists of a variety of factors like the product usage, contract duration, demographics and similar information. It makes sense, that based on the variety of factors in the score, there should be a variety of next steps to increase it as well. Without a proper explanation, the health score by itself is useless. Imagine two customers having a health score of 45/100. The first one is a regular service user who had technical issues in the past quarter and called support multiple times, and that decreased the score due to recurring issues. The other never got into touch with support, but also, hasn’t been using the service for the past 3 months. Same score, very different next steps. That’s what prescriptive analytics are about. Moving from the question of what is the score, to answering what should be done to improve it. For example, if a customer’s health score is low due to a decline in product usage, the guidelines might recommend personalized training sessions, additional support, or a product usage review. Technical issues on the other hand, after resolved can additionally be handled with addition of discounts, lowered rates or free-of-charge premium services. Treatments should be standardized but flexible enough to be tailored to individual customer needs and simple enough to execute for any representative that gets in contact with the customer. Schedule Regular Health Score Audits Health scores are not a set-it-and-forget-it tool. Regularly scheduled reviews of health scores and accompanying recommended actions should be an essential part of your customer success process. These reviews allow you to reassess the factors contributing to the score, identify trends, and adjust your strategy as necessary. Whether it’s monthly or quarterly, having a consistent review schedule ensures that you catch potential issues early and can act proactively. Furthermore, while deploying a new feature, it’s commonly overlooked adding its usage to health score calculations. With common health score audits, product teams get aligned more quickly with the customer success, and both teams are kept up-to-date on the impact the new features and campaigns have on the customers. Here’s a few ideas on what to check during a Health Score audit: Train Customer-Facing Teams For health scores to be truly effective, customer-facing teams must be well-trained in understanding and using them. Creating intuitive dashboards that properly reflect the customers’ state and give meaningful feedback on the best course of action is of course a major help, but it’s still up to a representative to make the best use of the provided information. This training should include how health scores are calculated, what they indicate about customer relationships, and how to use them to drive customer success initiatives. When your teams are confident in interpreting health scores, they can take more meaningful actions to improve customer outcomes. One way to ensure customer-facing teams are confident in the use of health scores is to keep them in the loop during the initial implementation, reviews and updates. They are usually the first ones to see discrepancies between the health score and the true state of the customer, making them a valuable part of ensuring health score reliability. Next steps Customer health scores can be a powerful tool for driving business success, but only if they are reliable. By ensuring up-to-date data, establishing clear guidelines for improvement, defining the components of the health score, scheduling regular reviews, and training your teams, you can transform your customer health scores into a reliable and actionable metric. In doing so, you’ll not only improve customer satisfaction but also drive long-term customer retention and growth. If you need help with auditing your health score and implementing latest data engineering methods into your customer lifecycle, get in touch with us.
5 Key Benefits of Consolidating Customer Data
Data consolidation is a process of integrating data from various sources into a single, unified view. Most businesses have issues with disparate data sources, which have stacked up throughout the years. Numerous factors contribute to the improper connection of multiple sources. Sometimes, after mergers and acquisitions, a joined company continues using their CRM and similar tools, adding a layer of complexity to consolidating data. Some companies exhibit organizational silos, where marketing and support departments seldom interact, resulting in suboptimal use of tech stacks and dispersed data. And sometimes, the sheer amount of data as well as legacy systems used make data consolidation an enormous project challenging to take on by already undermanned IT departments. Regardless of why data consolidation hasn’t yet made it to your company’s to-do list, there are numerous reasons to embrace the challenge. Here are five significant benefits of consolidating customer data. 1. Improved Marketing Campaigns Consolidating customer data provides a comprehensive view of customer behaviors and interactions, but it can also include various external data, such as competitors’ financial reports or media mentions. This unified dataset provides a clear customer view, ensuring marketers make decisions in a more informed way. With a unified data set, businesses can segment their audience more effectively, tailoring messages and offers to specific groups based on detailed insights. Additionally, this resolves the age-old problem of contacts getting offers they shouldn’t have. This results in higher engagement rates, increased customer loyalty, and ultimately, a better return on investment for marketing efforts. For example, an insurance company can use consolidated data to identify customers who are most likely to upsell based on demographics and their previous behaviors. Then, they can send personalized promotions via channels each individual customer is most likely to engage with. This leads to a rise in the probability of sales and a reduction in the campaign’s expenses, as the targeted customer base shrinks and conversion rates rise. Additionally, a company investing in digital advertising can use generated customer segments on digital platforms to optimize CPC and ROAS. 2. Improved Customer Support One undoubtedly frustrating experience for customers is when they contact customer support and the agent on call is unaware of their previous interactions through a different channel. Most commonly, this happens because different departments use different software, i.e., sales and marketing using Salesforce, and support departments rely on legacy ticketing systems. Consolidating data empowers customer support teams with complete and up-to-date information about each customer’s history and interactions with the company. When a customer reaches out for support, representatives can quickly access their entire profile, including past purchases, previous support tickets, and communication history. This enables faster resolution of issues and a more personalized support experience. Customers feel valued when they don’t have to repeat their information or history, leading to higher satisfaction and loyalty. Additionally, identifying trends in customer issues enables a proactive approach towards improving products and services. 3. Easier and More Accurate Reporting and Analytics Have you ever been in a meeting that ends with “let’s meet two weeks from now, so we can gather all the data”? I don’t believe there’s a single person who hasn’t. More often than not, two weeks later the data is still incomplete, and the stakeholders can’t really agree on the numbers shown. Disparate, fragmented, and disconnected systems are the reasons for this. When consolidated, data truly becomes a single source of truth. Suddenly, marketing and sales departments work with the same numbers, predictions become more accurate, and decisions get made during a single meeting. Consolidating data is the first step in generating an accurate report, and it is a starting point for any analyst keen on detecting business opportunities. Proper consolidation of data makes maintenance easier and less time-consuming compared to working with disparate sources. 4. Long-term Savings In every business, time is money. While the initial investment in data consolidation might seem substantial, the long-term savings are significant. Maintaining multiple, disconnected data systems is costly in terms of technology, manpower, and time. Consolidating data simplifies system maintenance, eliminates the need for redundant storage, and cuts down on the time required for data reconciliation and error correction. Additionally, streamlined data makes it simple to access any relevant data points, helping business leaders draw insights and make decisions faster. Businesses also benefit from reduced compliance costs, as managing a single data repository is easier and more secure than overseeing multiple databases. Generally, as organizations expand, the amount of disparate data increases, necessitating additional storage and staff. Additionally, the complexity of handling data increases, leading to a significant increase in all costs. Timely consolidation reduces maintenance costs, but it also reduces future cost increases as the organization grows. 5. Faster Project Implementation Pretty much every business or technology project is reliant on data. Quick access to data is detrimental from the beginning point of assessing the project’s profitability and viability all the way through to project reviews and reflection. Implementing new projects becomes more efficient and faster with a consolidated data infrastructure. Teams can access all necessary information from one place, accelerating the development of new analytics models, customer insights projects, and other data-driven initiatives. This agility allows project managers to quickly access data, reducing the need to involve additional staff or analysts to bring in the data. Furthermore, less staff also means fewer resources are spent on project onboarding, syncing, and reporting, accelerating project execution. How to start a data consolidation project When ready to save significant money and increase growth opportunities with data consolidation, a company may ask themselves where to start. Starting the journey toward data consolidation involves assessing a current data landscape and identifying the key sources of information within the organization. From there, it’s important to make a strategic plan to integrate these sources into a single platform while taking account of the available resources and uninterrupted business operations. At this point, the goal may seem unattainable due to limited IT resources or outdated legacy systems lacking proper data extraction features, but proper data
How to reduce churn with data engineering
Reducing churn is among the most common and hardest to impact KPIs subscription-based businesses face. Churn, or customer attrition, significantly impacts revenue and growth prospects. If put into numbers, subscription services usually have higher customer acquisition costs than monthly subscription price. That means that higher churn rates can easily make even customer acquisition unprofitable. For example, if the monthly cost of a subscription service is $15, and the cost of acquisition is $90, it takes 6 months for a company to reach net zero with a newly acquired customer. If a customer churns in month 4, the company actually loses at least $30 on that customer. Any associated costs, like support or cost of operations make that loss even higher. It makes sense for any business to take the matter of churn seriously and deal with it. The most common actions taken include customer support training, releasing features that could stick with customers more or sending out surveys to detect potential churn factors. With new developments in data science, you can take more proactive and, for customers, less disruptive actions to lower churn. Riding on sophisticated data engineering practices and the evolution of data architecture, businesses are leveraging comprehensive data lakehouses to gain actionable insights and proactively address churn. Let’s delve into how data engineering, particularly through data lakehouses, is revolutionizing churn management. The Role of Data Engineering Data engineering plays a transformative role in churn management by harnessing data to gain valuable insights. Traditionally, detecting churn reasons is overly simplified by correlating churn to CRM entries prior to canceling service. While that approach may drive some insights, it doesn’t take into account the complexity behind people’s decision to churn. According to Bain&Company, events priming the customer to churn can sometimes even be years apart. Reasons for oversimplification are many, but partially, it’s a result of technical complexities behind analyzing customers’ behavior and interactions throughout the entirety of a lifecycle. Data science and engineering make it viable to get a much more accurate and holistic image of causes of churn. With solutions like data lakehouse, companies can continuously draw insights from consumers’ behavior and in real time, spot events that can trigger churn. For example, in Deegloo, we developed customer segmentation data models for an insurance company. That enabled our client to focus their effort into reducing churn among the base of customers most likely to stay, effectively optimizing the effort needed to reduce overall churn. At the same time, it also provided insights on the best channel of communication for each particular customer, further scaling down the efforts needed to impact end results. Data Lakehouses: Unifying Data for Actionable Insights One of the technological advancements making it easier to use customer data for various purposes is a data lakehouse. A data lakehouse integrates structured and unstructured data from various sources into a unified repository. It’s an approach that enables businesses to collect and store vast amounts of raw data cost-effectively while maintaining data integrity and accessibility. Considering companies using subscription models, like insurances or telecoms collect vast amounts of data about the customers, markets, online and offline behavior and transactions, data lakehouses help in unifying all of it into a digestible user story. Combining the scalability of data lakes with the structured querying capabilities of data warehouses, data lakehouses become a powerful platform for churn analytics. Data consolidation Speaking of unifying data, data consolidation is both, crucial for accurate churn prediction and a starting point to get any other feature implemented as it merges disparate data sources into a single, unified view. This comprehensive dataset provides deeper insights into customer behaviors and patterns, enhancing predictive models’ accuracy. By eliminating data silos, businesses can more effectively identify at-risk customers, enabling timely intervention with personalized retention strategies. Ultimately, data consolidation fosters better decision-making and helps reduce churn, boosting customer loyalty and profitability. Enhanced Customer 360 view Data engineering significantly enhances customer 360 profiles by integrating and analyzing diverse data sources, providing a comprehensive view of customer behaviors and preferences. This understanding enables more accurate churn prediction, allowing businesses to identify at-risk customers proactively. By leveraging data engineering techniques, companies can tailor personalized retention strategies, improving customer satisfaction and loyalty, ultimately reducing churn rates and increasing long-term profitability. Customer segmentation Improved view of individual customers makes it viable to also push towards better segmentation. Using larger number of data points enables smarter customer segmentation and a more personalised approach in marketing and sales. Using models like UPLIFT helps identify the customer segment that is the most likely to either upsell or in case of churn, most likely to stay, significantly improving customer retention results with a decreased sales effort. With data engineering, the traditional ways of customer segmentation relying mostly on demographics can be brought to another level by utilising real-time behavioral data from both, internal and external systems making segments more relevant and enabling fully personalized offers. In conclusion, data science and engineering are transforming how businesses combat customer churn. By leveraging comprehensive data sets and advanced analytics, businesses can proactively identify churn indicators, personalize retention strategies, and ultimately enhance customer satisfaction and loyalty. As organizations continue to invest in data infrastructure and analytics capabilities, the battle against churn will increasingly be fought—and won—through the strategic application of data engineering and willingness to adapt business practices to the insights data generates.